"Review on Complex Multi-Agent Environments in Reinforcement Learning"

Abstract

This paper provides a comprehensive review of recent advancements in reinforcement learning (RL) within complex multi-agent environments. As the field of RL evolves, the need for effective strategies to manage and optimize interactions among multiple agents becomes increasingly crucial. This review systematically examines the various methodologies and frameworks developed to address the challenges inherent in multi-agent settings, including coordination, competition, and communication among agents. Key topics discussed include multi-agent policy optimization, decentralized learning approaches, and the integration of game-theoretic principles. Additionally, the paper highlights the impact of environmental complexity on learning performance and scalability, and explores emerging trends such as the application of deep learning techniques and the development of scalable algorithms. By synthesizing the latest research and identifying gaps in current methodologies, this review aims to provide a valuable resource for researchers and practitioners seeking to advance the state of the art in multi-agent RL systems.